Node Localization in Wireless Sensor Networks Using Swallow Swarm Optimization Algorithm

This work solves the problem by developing a distributed 3D positioning method in a large 3D WSN. The continued growth of 3D sensor networks makes the topology more complex due to the tight connection to the surrounding deployment environment. In this study, we propose a node-weighted swallow group-optimized convex node segmentation (NWS2CNS) to solve the optimization of the number of bumps in a large 3D WSN. NWS2CNS is proposed to improve the positional accuracy obtained using node inertia weights to accurately calculate the acceleration factor. The suboptimal network segmentation is accomplished by accurately identifying concave nodes that break down the 3D WSN into multiple roughly convex subnets. The proposed positioning algorithm also applies a new three-dimensional coordinate transformation algorithm to help reduce errors due to coordinate integration between coordinates. Subnets to improve location accuracy.